The location of the electric vehicle charging station (EVCS) is the important link in the construction of the EVCS. The optimal location of the electric vehicle directly affects the operating efficiency of the charging station and the satisfaction of the electric vehicle user, site selection play an important part throughout whole life cycle, which is deemed to be multiple attribute group decision making (MAGDM) issue involving many experts and many conflicting attributes. In practical MAGDM issues, the information of uncertain and fuzzy cognitive decision is well-depicted by uncertain linguistic term sets (ULTSs). These ULTSs could be simply shifted into the probabilistic uncertain linguistic sets (PULTSs). In such paper, we design some novel probabilistic uncertain linguistic weighted Dice similarity measures (PULWDSM) and the probabilistic uncertain linguistic weighted generalized Dice similarity measures (PULWGDSM). Subsequently, the PULWGDSM-based MAGDM methods are presented under PULTSs. In the end, a practical case which concerns about the location planning of electric vehicle charging stations is offered to demonstrate the proposed PULWGDSM’s applicability and advantages.

电动汽车充电站（EVCS）的位置是EVCS建设中的重要环节。电动汽车的最佳位置直接影响充电站的运行效率和电动汽车用户的满意度，选址在整个生命周期中发挥着重要作用，这被认为是多属性组决策（MAGDM）问题涉及许多专家和许多相互矛盾的属性。在实际的MAGDM问题中，不确定的语言术语集（ULTSs）很好地描述了不确定和模糊的认知决策信息。这些ULTS可以简单地转移到概率不确定语言集（PULTS）中。在这样的论文中，我们设计了一些新颖的概率不确定语言加权Dice相似度度量（PULWDSM）和概率不确定语言加权广义Dice相似度度量（PULWGDSM）。随后，在PULTSs下介绍了基于PULWGDSM的MAGDM方法。最后，提供了一个有关电动汽车充电站位置规划的实际案例，以证明所提出的PULWGDSM的适用性和优势。